Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample Learning
This paper proposes a weakly supervised cross-domain person re-identification (Re-ID) method based on small sample data. In order to reduce the cost of data collection and annotation, the model design focuses on extracting and abstracting the information contained in the data under limited condition...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/19/4186 |
| _version_ | 1797575944049786880 |
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| author | Huiping Li Yan Wang Lingwei Zhu Wenchao Wang Kangning Yin Ye Li Guangqiang Yin |
| author_facet | Huiping Li Yan Wang Lingwei Zhu Wenchao Wang Kangning Yin Ye Li Guangqiang Yin |
| author_sort | Huiping Li |
| collection | DOAJ |
| description | This paper proposes a weakly supervised cross-domain person re-identification (Re-ID) method based on small sample data. In order to reduce the cost of data collection and annotation, the model design focuses on extracting and abstracting the information contained in the data under limited conditions. In this paper, we focus on the problems of strong data dependence, weak cross-domain capability and low accuracy in Re-ID in weakly supervised scenarios. Our contributions are as follows: first, we implement a joint training framework with the help of small sample learning and cross-domain migration for Re-ID. Second, with the help of residual compensation and fusion attention module, the RCFA module is designed, and the model framework is built on this basis to improve the cross-domain ability of the model. Third, to solve the problem of low accuracy caused by insufficient data coverage of small samples, a fusion of shallow features and deep features is designed to enable the model to weighted fusion of shallow detail information and deep semantic information. Finally, by selecting different camera images in Market1501 dataset and DukeMTMC-reID dataset as small samples, respectively, and introducing another dataset data for joint training, we demonstrate the feasibility of this joint training framework, which can perform weakly supervised cross-domain Re-ID based on small sample data. |
| first_indexed | 2024-03-10T21:45:30Z |
| format | Article |
| id | doaj.art-2eeaf95eb85f4b1689a84b1f6afb4010 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:45:30Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-2eeaf95eb85f4b1689a84b1f6afb40102023-11-19T14:18:26ZengMDPI AGElectronics2079-92922023-10-011219418610.3390/electronics12194186Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample LearningHuiping Li0Yan Wang1Lingwei Zhu2Wenchao Wang3Kangning Yin4Ye Li5Guangqiang Yin6School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610051, ChinaShenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610051, ChinaShenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610051, ChinaThis paper proposes a weakly supervised cross-domain person re-identification (Re-ID) method based on small sample data. In order to reduce the cost of data collection and annotation, the model design focuses on extracting and abstracting the information contained in the data under limited conditions. In this paper, we focus on the problems of strong data dependence, weak cross-domain capability and low accuracy in Re-ID in weakly supervised scenarios. Our contributions are as follows: first, we implement a joint training framework with the help of small sample learning and cross-domain migration for Re-ID. Second, with the help of residual compensation and fusion attention module, the RCFA module is designed, and the model framework is built on this basis to improve the cross-domain ability of the model. Third, to solve the problem of low accuracy caused by insufficient data coverage of small samples, a fusion of shallow features and deep features is designed to enable the model to weighted fusion of shallow detail information and deep semantic information. Finally, by selecting different camera images in Market1501 dataset and DukeMTMC-reID dataset as small samples, respectively, and introducing another dataset data for joint training, we demonstrate the feasibility of this joint training framework, which can perform weakly supervised cross-domain Re-ID based on small sample data.https://www.mdpi.com/2079-9292/12/19/4186person re-identificationweakly supervisionsmall samplecross-domain migration |
| spellingShingle | Huiping Li Yan Wang Lingwei Zhu Wenchao Wang Kangning Yin Ye Li Guangqiang Yin Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample Learning Electronics person re-identification weakly supervision small sample cross-domain migration |
| title | Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample Learning |
| title_full | Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample Learning |
| title_fullStr | Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample Learning |
| title_full_unstemmed | Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample Learning |
| title_short | Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample Learning |
| title_sort | weakly supervised cross domain person re identification algorithm based on small sample learning |
| topic | person re-identification weakly supervision small sample cross-domain migration |
| url | https://www.mdpi.com/2079-9292/12/19/4186 |
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